Notes

The latest Advancement in the Layout as well as Health care Application of In Situ Self-Assembled Polypeptide Resources.
Patient-centered care is an essential component of quality health care. To support patient-centered care initiatives at our institution, we created a feature in our EHR to centrally view information about the patient's values, goals and preferences. We applied user-centered design methods to ensure that the aggregate view was easy to use and would meet user needs. We created a six-week plan to iterate through increasingly detailed design mock-ups. We defined 7 user stories that later served as a basis for user testing scripts. We conducted user testing on our third design iteration; we reached theme saturation with 8 testing sessions. We incorporated findings into the fourth design (week 6) but continued to refine the design in parallel to development (through week 20+). The advance directives section required the most attention. We will use a pilot and additional user testing to validate the design and to inform future versions.Research has shown that health outcomes are significantly driven by patient's social and economic needs and environment, commonly referred to as the social determinants of health (SDoH). Standardized documentation of social and economic needs in healthcare are underutilized. This study examines the prevalence of documented social and economic needs (Z-codes) in a nationwide inpatient database and the association with emergency department (ED) admissions. Multivariate logistic regression was used to assess the effect of social and economic Z-codes on hospital admission through the ED. Payer source, gender, age at admission, comorbidity count, and median ZIP code income quartile covariates were included in the logistic regression analyses. Patients with documented social and economic Z-codes were significantly more likely to be admitted through the ED than those without documented social and economic needs, after adjusting for covariates. Standardized and widespread collection of these valuable Z-codes within EHR systems or administrative claims databases can help with targeted resource allocation to alleviate possible barriers to care and mitigate ED utilization.It is difficult to arrive at an efficient and widely acceptable set of common data elements (CDEs). Trial outcomes, as defined in a clinical trial registry, offer a large set of elements to analyze. However, all clinical trial outcomes is an overwhelming amount of information. One way to reduce this amount of data to a usable volume is to only use a subset of trials. Our method uses a subset of trials by considering trials that support drug approval (pivotal trials) by Food and Drug Administration. We identified a set of pivotal trials from FDA drug approval documents and used primary outcomes data for these trials to identify a set of important CDEs. We identified 76 CDEs out of a set of 172 data elements from 192 pivotal trials for 100 drugs. This set of CDEs, grouped by medical condition, can be considered as containing the most significant data elements.Wrist accelerometers for assessing hallmark measures of physical activity (PA) are rapidly growing with the advent of smartwatch technology. Given the growing popularity of wrist-worn accelerometers, there needs to be a rigorous evaluation for recognizing (PA) type and estimating energy expenditure (EE) across the lifespan. Participants (66% women, aged 20-89 yrs) performed a battery of 33 daily activities in a standardized laboratory setting while a tri-axial accelerometer collected data from the right wrist. A portable metabolic unit was worn to measure metabolic intensity. We built deep learning networks to extract spatial and temporal representations from the time-series data, and used them to recognize PA type and estimate EE. The deep learning models resulted in high performance; the F1 score was 0.82, 0.81, and 95 for recognizing sedentary, locomotor, and lifestyle activities, respectively. The root mean square error was 1.1 (+/-0.13) for the estimation of EE.Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider. The system evaluates care alternatives through interactions with patients via a mobile application. Reasoning on an initial set of provided symptoms, the triage application generates AI-powered, personalized questions to better characterize the problem and recommends the most appropriate point of care and time frame for a consultation. GSK-3 phosphorylation The underlying technology was developed to meet the needs for performance, transparency, user acceptance and ease of use, central aspects to the adoption of AI-based decision support systems. Providing such remote guidance at the beginning of the chain of care has significant potential for improving cost efficiency, patient experience and outcomes. Being remote, always available and highly scalable, this service is fundamental in high demand situations, such as the current COVID-19 outbreak.Patients face challenges in accurately interpreting their lab test results. To fulfill their knowledge gap, patients often turn to online resources, such as Community Question-Answering (CQA) sites, to seek meaningful information and support from their peers. Retrieving the most relevant information to patients' queries is important to help patients understand lab test results. However, few studies investigated the retrieval of lab test-related questions on CQA platforms. To address this research gap, we build and evaluate a system that automatically ranks questions about lab tests based on their similarity to a given question. The system is tested using diabetes-related questions collected from Yahoo! Answers' health section. Experimental results show that the regression-weighted combination of deep representations and shallow features was most effective in the Yahoo! Answers dataset. The proposed system can be extended to medical question retrieval, where questions contain a variety of lab tests.The potential of Reinforcement Learning (RL) has been demonstrated through successful applications to games such as Go and Atari. However, while it is straightforward to evaluate the performance of an RL algorithm in a game setting by simply using it to play the game, evaluation is a major challenge in clinical settings where it could be unsafe to follow RL policies in practice. Thus, understanding sensitivity of RL policies to the host of decisions made during implementation is an important step toward building the type of trust in RL required for eventual clinical uptake. In this work, we perform a sensitivity analysis on a state-of-the-art RL algorithm (Dueling Double Deep Q-Networks) applied to hemodynamic stabilization treatment strategies for septic patients in the ICU. We consider sensitivity of learned policies to input features, embedding model architecture, time discretization, reward function, and random seeds. We find that varying these settings can significantly impact learned policies, which suggests a need for caution when interpreting RL agent output.The mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare. However, data insufficiency and the clinical diversity of rare diseases make it hard for deep learning models to be trained. Mortality prediction for these patients with different diseases can be viewed as a multi-task learning problem with insufficient data but a large number of tasks. On the other hand, insufficient training data makes it difficult to train task-specific modules in multi-task learning models. To address the challenges of data insufficiency and task diversity, we propose an initialization-sharing multi-task learning method (Ada-SiT). Ada-Sit can learn the parameter initialization and dynamically measure the tasks' similarities, used for fast adaptation. We use Ada-SiT to train long short-term memory networks (LSTM) based prediction models on longitudinal EHR data. The experimental results demonstrate that the proposed model is effective for mortality prediction of diverse rare diseases.A reliable and searchable knowledge database of adverse drug reactions (ADRs) is highly important and valuable for improving patient safety at the point of care. In this paper, we proposed a neural multi-task learning system, NeuroADR, to extract ADRs as well as relevant modifiers from free-text drug labels. Specifically, the NeuroADR system exploited a hierarchical multi-task learning (HMTL) framework to perform named entity recognition (NER) and relation extraction (RE) jointly, where interactions among the learned deep encoder representations from different subtasks are explored. Different from the conventional HMTL approach, NeuroADR adopted a novel task decomposition strategy to generate auxiliary subtasks for more inter-task interactions and integrated a new label encoding schema for better handling discontinuous entities. Experimental results demonstrate the effectiveness of the proposed system.IgA nephropathy (IgAN) is common worldwide and has heterogeneous phenotypes. Predicting long-term outcomes is important for clinical decision-making. As right-censored patients become common during the long-term follow-up, either excluding these patients from the cohort or labeling them as control will bias the risk estimation. Thus, we constructed a survival model using EXtreme Gradient Boosting for survival (XSBoost-Surv), to accurately predict the prognosis of IgAN patients by taking the time-to-event information into the modeling procedure. Shapley Additive exPlanations (SHAP) was employed to interpret the individual predicted result and the non-linear relationships between the predictors and outcome. Experiments on real-world data showed our model achieved superior discrimination performance over other conventional survival methods. By providing insights into the exact changes in risk induced by certain characteristics of the patients, this explainable and accurate survival model can help improve the clinical understanding of renal progression and benefit the therapies for the IgAN patients.Type 1 diabetes (T1D) is a chronic autoimmune disease that affects about 1 in 300 children and up to 1 in 100 adults during their life-time1. Improvements in early prediction of T1D onset may help prevent diagnosis for diabetic ketoacidosis, a serious complication often associated with a missed or delayed T1D diagnosis. In addition to genetic factors, progression to T1D is strongly associated with immunologic factors that can be measured during clinical visits. We developed a T1D-specific ontology that captures the dynamic patterns of these biomarkers and used it together with a survival model, RankSvx, proposed in our prior work2. We applied this approach to a T1D dataset harmonized from three birth cohort studies from the United States, Finland, and Sweden. Results show that the dynamic biomarker patterns captured in the proposed ontology are able to improve prediction performance (in concordance index) by 5.3%, 3.3%, 2.8%, and 1.0% over baseline for 3, 6, 9, and 12 month duration windows, respectively.Low trial generalizability is a concern. The Food and Drug Administration had guidance on broadening trial eligibility criteria to enroll underrepresented populations. However, investigators are hesitant to do so because of concerns over patient safety. There is a lack of methods to rationalize criteria design. In this study, we used data from a large research network to assess how adjustments of eligibility criteria can jointly affect generalizability and patient safety (i.e the number of serious adverse events [SAEs]). We first built a model to predict the number of SAEs. Then, leveraging an a priori generalizability assessment algorithm, we assessed the changes in the number of predicted SAEs and the generalizability score, simulating the process of dropping exclusion criteria and increasing the upper limit of continuous eligibility criteria. We argued that broadening of eligibility criteria should balance between potential increases of SAEs and generalizability using donepezil trials as a case study.
Read More: https://www.selleckchem.com/GSK-3.html